Responsible ai

Skill architect-4-citadell/elektra-skills/responsible-ai

Governance skills for AI coding agents. Responsible AI-driven execution — Godspeed. Built across 66+ production sessions. Zero silent failures.

Install

npx -y skills add architect-4-citadell/elektra-skills --skill responsible-ai

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Responsible AI governance skill for software development. Enforces a 7-pillar RAI framework (tenant/data isolation, PII protection, citation integrity, confidence scoring, hallucination prevention, bias mitigation, content provenance) during development. Use when building or reviewing code touching LLM pipelines, AI-generated output, data source connectors, user data queries, multi-tenant isolation, disclaimers, or transparency features. Use for auditing RAI compliance before releases. Triggers on "responsible AI", "RAI", "citation", "hallucination", "bias", "PII", "confidence score", "content provenance", "disclaimer", "trust page".

The file declares its own license as CC BY-NC-SA 4.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

6.2 KB, as published. Nobody here has run it

Responsible AI Harness -- Development Enforcement Skill

When This Skill Activates

This skill triggers during development when ANY change touches the AI output pipeline:

Trigger	What to Check
Code touching LLM orchestration / model selection	Multi-model consensus, confidence scoring, model selection transparency
Code touching AI pipeline / DAG execution	Citation audit, RAG grounding, self-critique pass, source attribution
Code touching prompt templates	Balanced framing constraints, grounding instructions, citation markers
Code touching document rendering / export	AI disclosure footer, metadata, disclaimer system
Code touching deterministic computation modules	Computation integrity -- LLM NEVER computes financial figures
Code touching external data sources / connectors	Source reliability assessment, data freshness metadata
Code touching user data / graph queries	Tenant isolation, data visibility rules, PII in queries
New data source integration	Source reliability, bias assessment, PII risk, freshness guarantees
Prompt template changes	Bias review, grounding constraints, citation requirements
Document export changes	Disclaimer presence, AI disclosure, provenance metadata

RAI Risk Tiers

Determine the tier of your change, then apply the corresponding controls:

Tier 1 -- Critical (User data layer, deterministic computation, LLM orchestration, AI pipeline, document rendering): Apply the FULL Pre-Merge Checklist below.

Tier 2 -- Significant (External data sources, prompt templates, channel/output formatting): Apply the checklist items marked with [T2].

Tier 3 -- Standard (API gateway, web UI-only, infrastructure): Standard code review. No RAI-specific requirements.

Pre-Merge RAI Checklist

Run through each applicable item before marking work as complete:

1. Tenant / Data Isolation [T1] [T2]

Session-level or row-level security variables respected in all queries
Graph/database queries scoped by tenant identifier (MERGE, MATCH, CREATE all include tenant)
Data visibility model maintained (e.g., personal / active client / other clients)
No cross-tenant data in generated output, logs, or error messages

2. PII Protection [T1] [T2]

User PII masked before sending to external LLM APIs
PII not present in observability traces or error logs
Vector embeddings and cache entries inherit source data retention policies
Prompt injection defenses in place for any user-facing input to LLM path

3. Citation Integrity [T1]

Factual claims in generated text traceable to retrieved source chunks
Citation markers (e.g., [cite: chunk_id]) required in generation prompts
Post-generation citation audit verifies each citation maps to a real chunk
No citations generated from LLM parametric memory (retrieve-then-generate only)
Source metadata (url, title, publisher, access_date) preserved through pipeline

4. Confidence Scoring [T1]

Section-level confidence scores computed (source_alignment, token_match, model_agreement, data_freshness)
Scores below 0.50 block content from appearing in output
Amber (0.70-0.84) and Red (0.50-0.69) indicators trigger user warnings
Output metadata (e.g., Intelligence Receipt) included in document output

5. Hallucination Prevention [T1]

Generation prompts include grounding instruction: "Only include information from provided context"
Financial/numerical figures computed by deterministic engine, NOT by LLM
Generated text validated against deterministic computed values (math consistency)
Self-Critique and Revision pass for high-stakes document types

6. Bias Mitigation [T1] [T2]

Prompt templates include balanced framing constraints
Competitive analysis requires 2+ sources per competitor claim
People-related sections prohibit assumptions about demographics
Multi-model consensus reduces single-model bias

7. Content Provenance [T1]

AI disclosure footer present in all generated documents
Output metadata accessible in web preview
Verification URL generated for each document (if applicable)
Document metadata includes AI-generated content flag

Disclaimers [T1]

Disclaimer footer present in exported documents (configurable per application tier)
Terms of Service professional liability exclusion intact

Audit Trail [T1] [T2]

Generation requests logged with user_id, document_type, sources_used, models_used
Model decisions logged (which models agreed/disagreed)
PII masking actions logged
Human review/edit actions logged

Critical Rules (Non-Negotiable)

LLM NEVER computes financial figures. All calculations (DCF, WACC, CAGR, Monte Carlo, NPS, churn, penalty estimations) MUST use deterministic computation -- never LLM inference.
No silent fallbacks on safety controls. If PII masking fails, the request MUST fail -- not silently proceed with unmasked PII.
Cross-tenant data leakage is catastrophic failure. Every graph query, every RAG retrieval, every generation context MUST be tenant-scoped.
Citations are mandatory for factual claims. "According to..." without a verifiable source is a hallucination, not a citation.
Confidence below 0.50 = do not include. Thin evidence is worse than no evidence in professional documents.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.